Research on Web service selection based on cooperative evolution

  • Authors:

  • Xiao-Qin Fan;Xian-Wen Fang;Chang-Jun Jiang

  • Affiliations:

  • College of Computer and Information Technology, Shanxi University, 030006 Taiyuan, China and The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, ...;The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, 201804 Shanghai, China;The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, 201804 Shanghai, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

Web service selection, as an important part of Web service composition, has direct influence on the quality of composite service. Therefore, it has attracted many researchers to focus on the research of quality of service (QoS) driven Web service selection in the past years, and many algorithms based on integer programming (IP), mixed integer linear programming (MILP), multi-dimension multi-choice 0-1 knapsack problem (MMKP), Markov decision programming (MDP), genetic algorithm (GA), and particle swarm optimization (PSO) and so on, have been presented to solve it, respectively. However, these results have not been satisfied at all yet. In this paper, a new cooperative evolution (Co-evolution) algorithm consists of stochastic particle swarm optimization (SPSO) and simulated annealing (SA) is presented to solve the Web service selection problem (WSSP). Furthermore, in view of the practical Web service composition requirements, an algorithm used to resolve the service selection with multi-objective and QoS global optimization is presented based on SPSO and the intelligent optimization theory of multi-objective PSO, which can produce a set of Pareto optimal composite services with constraint principles by means of optimizing various objective functions simultaneously. Experimental results show that Co-evolution algorithm owns better global convergence ability with faster convergence speed. Meanwhile, multi-objective SPSO is both feasible and efficient.